An important issue arising from large scale data integration is how to select the top-K ranking answers from multiple data sources, where K is relatively small compared to the total number of tuples.
To answer a top-K query, a straightforward way is to combine or join all the data from different sources, and then to select the top-K answers ordered by a user-defined rank function. However, this approach suffers from a great consumption of bandwidth when generating the whole results.
Fagin was the first to propose efficient algorithms to answer ranking queries in middleware environments [1, 2]. In , an efficient algorithm was introduced to process top-K queries over web-accessible databases by maximizing source access parallelism to minimize query response time. Ripple join is a new family of join algorithms designed for minimizing the time until an acceptably precise estimate of the query result is available . Based on the basic idea of ripple join, a rank-join algorithm was proposed to support top-K join queries in relational databases .
This paper proposes an efficient pruning-based approach for answering top-K join queries in large scale data integration.
The pruning-based top-K ranking approach takes the following parameters as input: relation R, relation S, the join condition r(a)s(b), the monotonic increasing rank function f(r(p), s(q)), and the number of desired join answers K, where r(a) and s(b) are the join attributes, and r(p) and s(q) are the rank attributes of R and S.
Assume that relation R and relation S are sorted on their rank attributes in descending order, the basic idea of the proposed approach is to produce the top-K rank value of the rank function by iteratively pruning irrelevant tuples in R and S that can not produce any join results with a rank value greater than or equal to the rank value generated till now. Finally, tuples corresponding to the top-K rank value will be joined. To help illustrate the proposed approach, the following notions are depicted in Figure 1:
The general architecture of the proposed approach is shown in Figure 2, which includes the following steps to answer a top-K join query:
Step 1: (1) Initialize the lower bound of the rank function as T=0. (2) Empty R-Rank-Queue (,, f), S-Rank-Queue (,, f), and Priority-Rank-Queue (,, f).
Step 2: Sort R and S on rank attributes in descending order.
Step 3: Select top-K join tuples from R and S respectively by invoking algorithm Top-K-Join-Tuple (R, S, f, K, T) and Top-K-Join-Tuple (S, R, f, K, T) in Figure 3.
Step 4: (1) Output the row number of the top-K join tuples from R, the row number of the joined tuples from S, and the rank value of the join results to R-Rank-Queue (,, f). (2) Generate S-Rank-Queue (,, f) in the same way.
Step 5: Output R-Rank-Queue (,, f) and S-Rank-Queue(,, f) to Priority-Rank-Queue(,, f).
Step 6: Sort Priority-Rank-Queue on the rank value in descending order.
Step 7: If the length of R-Rank-Queue or the length of S-Rank-Queue is not equal to K, which implies no join results with greater rank value will be produced further, then the top-K ordered tuples in Priority-Rank-Queue is the top-K rank value for RS, and the top-K ranking answers will be successfully returned.
Step 8: Set the new lower bound T with the rank value of the K-th tuple in Priority-Rank-Queue.
Step 9: (1) Prune the first tuple in R and S respectively. (2) Prune the tuples in R below the top-K join tuples because the rank value produced by these tuples joined with tuples in S can not be greater than or equal to the minimum rank value in R-Rank-Queue. (3) Prune the tuples in S below the top-K join tuples in the same way.
Step 10: Goto Step 3.
The algorithm for selecting top-K join tuples from relation R that can be joined with tuples from relation S is shown in Figure 3.
The contribution of this paper is to propose an efficient pruning-based approach for answering top-K join queries, which can be integrated in the semantic overlay of the Knowledge Grid and Peer-to-Peer networks to support advanced applications [6, 7].
The research work is supported by the National Science Foundation of China (No. 60503047) and the National Basic Research Program of China (No. 2003CB317000).
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Fagin, R., Lotem, A., and Naor, M. Optimal Aggregation Algorithms for Middleware. In Proceedings of PODS 2001, Santa Barbara, USA, May 2001.
Haas, P., and Hellerstein, J. Ripple Joins for Online Aggregation. In Proceedings of SIGMOD 1999, Pennsylvania, USA, June 1999.
Ilyas, I., Aref, W., and Elmagarmid, A. Supporting Top-K Join Queries in Relational Databases. In Proceedings of VLDB 2003, Berlin, Germany, September 2003.
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Zhuge, H., Liu, J., Feng, L., Sun, X., and He, C. Query Routing in a Peer-to-Peer Semantic Link Network. Computational Intelligence 21 (2) (2005) 197-216.